Evaluation tool for vehicle survivability planning

ABSTRACT

Systems and methods for improving vehicle survivability. In some embodiments, an evaluation model may be built based at least in part on mission information. The mission information may comprise vehicle information regarding at least one vehicle and threat information regarding a plurality of threats to the at least one vehicle. The evaluation model may be used to assign a numerical measure to each potential action of a plurality of potential actions for the at least one vehicle, where the numerical measure may be based on measures of signature exposure relating to multiple threats of the plurality of threats. A sequence of actions to be executed by the at least one vehicle may be selected based at least in part on the respective numerical measures assigned to the plurality of potential actions, to improve the at least one vehicle&#39;s survivability against the plurality of threats.

BACKGROUND

Military and non-military vehicles moving through a hostile region mayencounter multiple threats, such as enemy vehicles, ground troops,and/or artillery systems. Such threats may have weapon systems and maybe equipped with multi-spectral sensors for obtaining information abouttarget vehicles. For instance, a threat may be equipped with one or morepassive sensors to obtain information about a target vehicle bydetecting emissions from the target vehicle. An example of a passivesensor is an infrared (IR) sensor for detecting energy in the infraredband, such as thermal radiation, emitted by a target vehicle. A threatmay also be equipped with one or more active sensors to obtaininformation about a target vehicle by irradiating the target vehiclewith electromagnetic waves and detecting those waves that bounce backfrom the target vehicle. An example of an active sensor is a radar fortransmitting and detecting radio frequency (RF) waves.

Threats may be located at known or unknown locations, and may have knownor unknown capabilities for detecting and attacking target vehicles. Asurvivability planning system may analyze known threats prior to thestart of a mission to improve vehicle survivability. For example, asurvivability planning system may plan a safe route for a vehiclebetween a starting point and an ending point so as to avoid areas thatare within detection and attack ranges of known threats.

Unknown threats, for example, those that “pop-up” during a mission, maybe more challenging to defend against because a survivability planningsystem may lack sufficient time and information to automatically computean optimal plan before a pop-up threat engages a target vehicle. As aresult, conventional vehicles rely on human operators to decide when andwhere to position and route the vehicles with respect to pop-up threats.Survivability of such a vehicle thus depends directly on the informationan individual operator has regarding an area of operation, locations ofthreats, and each threat's detection and attack capabilities against thevehicle in the specific environment.

SUMMARY

In some embodiments a system for improving vehicle survivability isdisclosed. The system comprises at least one processor programmed tobuild an evaluation model based at least in part on mission information,the mission information comprising vehicle information regarding atleast one vehicle, the mission information further comprising threatinformation regarding a plurality of threats to the at least onevehicle; use the evaluation model to assign a numerical measure to eachpotential action of a plurality of potential actions for the at leastone vehicle, the numerical measure being based at least in part on firstand second measures of signature exposure of the at least one vehicleresulting from executing the potential action, wherein the first measureof signature exposure relates to a first threat of the plurality ofthreats and the second measure of signature exposure relates to a secondthreat of the plurality of threats; and select, based at least in parton the respective numerical measures assigned to the plurality ofpotential actions, a sequence of actions to be executed by the at leastone vehicle to improve the at least one vehicle's survivability againstthe plurality of threats.

In some embodiments, a method for improving vehicle survivability isdisclosed. The method comprises building an evaluation model based atleast in part on mission information, the mission information comprisingvehicle information regarding at least one vehicle, the missioninformation further comprising threat information regarding a pluralityof threats to the at least one vehicle; using the evaluation model toassign a numerical measure to each potential action of a plurality ofpotential actions for the at least one vehicle, the numerical measurebeing based at least in part on first and second measures of signatureexposure of the at least one vehicle resulting from executing thepotential action, wherein the first measure of signature exposurerelates to a first threat of the plurality of threats and the secondmeasure of signature exposure relates to a second threat of theplurality of threats; and selecting, based at least in part on therespective numerical measures assigned to the plurality of potentialactions, a sequence of actions to be executed by the at least onevehicle to improve the at least one vehicle's survivability against theplurality of threats.

In some embodiments, at least one non-transitory computer-readablestorage medium is disclosed. The at least one non-transitorycomputer-readable storage medium stores processor-executableinstructions that, when executed by at least one processor, cause the atleast one processor to build an evaluation model based at least in parton mission information, the mission information comprising vehicleinformation regarding at least one vehicle, the mission informationfurther comprising threat information regarding a plurality of threatsto the at least one vehicle; use the evaluation model to assign anumerical measure to each potential action of a plurality of potentialactions for the at least one vehicle, the numerical measure being basedat least in part on first and second measures of signature exposure ofthe at least one vehicle resulting from executing the potential action,wherein the first measure of signature exposure relates to a firstthreat of the plurality of threats and the second measure of signatureexposure relates to a second threat of the plurality of threats; andselect, based at least in part on the respective numerical measuresassigned to the plurality of potential actions, a sequence of actions tobe executed by the at least one vehicle to improve the at least onevehicle's survivability against the plurality of threats.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts discussed in greater detail below (provided suchconcepts are not mutually inconsistent) are contemplated as being partof the inventive subject matter disclosed herein. In particular, allcombinations of claimed subject matter appearing at the end of thisdisclosure are contemplated as being part of the inventive subjectmatter disclosed herein.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not necessarily drawn to scale. Forpurposes of clarity, not every component may be labeled in everydrawing.

FIG. 1 shows an example of an illustrative survivability planningsystem, in accordance with some embodiments.

FIG. 2 shows an illustrative process that may be performed by asurvivability planning system to improve vehicle survivability during amission, in accordance with some embodiments.

FIG. 3 shows an example of a mathematical model that can be used torepresent a vehicle-to-threat scenario, in accordance with someembodiments.

FIGS. 4A-B show an illustrative route for a vehicle modeled as asequence of maneuvers each associated with a cost metric, in accordancewith some embodiments.

FIG. 5 shows an illustrative process that may be performed by asurvivability planning system to select a possible course of action fora vehicle in an actual or hypothetical vehicle-to-threat scenario, inaccordance with some embodiments.

FIG. 6A shows an example of two signature exposure models that can beused to estimate how easily a vehicle can be detected from variousangles by a threat, in accordance with some embodiments.

FIG. 6B shows an example of a combined signature exposure model, inaccordance with some embodiments.

FIG. 7 shows an illustrative mission timeline divided into multiplestages, in accordance with some embodiments.

FIG. 8 shows an illustrative graphical user interface that can be usedto visually render a quantity of interest to a human operator, inaccordance with some embodiments.

FIG. 9 shows another illustrative graphical user interface that can beused to visually render a quantity of interest to a human operator, inaccordance with some embodiments.

FIG. 10 shows, schematically, an illustrative computer on which variousinventive aspects of the present disclosure can be implemented.

DETAILED DESCRIPTION

The inventors have recognized and appreciated that, when a humanoperator attempts to determine how to respond to a pop-up threat in adynamic and high-stress environment, the human operator may not be ableto take into account all factors that may impact vehicle survivability.Accordingly, in some embodiments, systems and methods are provided forautomatically selecting one or more actions that may improve vehiclesurvivability in a multi-threat environment. In some embodiments, theselected actions may be presented to a human operator asrecommendations. In alternative embodiments, for example, in an unmannedaerial vehicle (UAV), the selected actions may be automaticallyimplemented to control the UAV.

In one aspect, the inventors have recognized and appreciated thatmultiple factors may impact vehicle survivability, including, but notlimited to, vehicle speed, altitude, and aspect, vehiclemaneuverability, threat lethality and detection capability againstvehicle, vehicle lethality and detection capability against threat,geometry of vehicle relative to threat, terrain conditions, weatherconditions, mission objective, and mission importance. Such factors,either alone or in combination, may affect a vehicle's abilities todetect a threat, evade detection by the threat, mitigate the threatusing a countermeasure, and survive an attack by the threat.Accordingly, in some embodiments, mechanisms may be provided by which asurvivability planning system may take into account any suitablecombination of factors in selecting an appropriate course of action fora vehicle. For example, the survivability planning system may take intoaccount information about one or more threats to the vehicle, one ormore signature exposure models associated with the vehicle relative tothe one or more threats, geometry of the vehicle and/or the one or morethreats, situational data, and various other types of information. Inone implementation, a survivability planning system may model a physicalregion as a collection of cells, and may model movement of a vehiclethrough the physical region as a sequence of maneuvers between cells.The survivability planning system may assign to each maneuver anumerical measure indicative of how much positive or negative impact themaneuver may have on the overall survivability of the vehicle, where thenumerical measure may reflect any suitable combination of any of theaforementioned factors and other factors.

In another aspect, the inventors have recognized and appreciated that avehicle may be operated based at least in part on one or more signatureexposure models associated with the vehicle. In some embodiments, one ormore signature exposure models associated with the vehicle may be usedby a survivability planning system to select an appropriate course ofaction in view of one or more threats to the vehicle. A survivabilityplanning system may use information about the one or more threats toidentify one or more relevant signature exposure model associated withthe vehicle, and select an appropriate course of action for the vehiclebased at least in part on the identified one or more signature exposuremodels. For example, a survivability planning system may determine thatthe vehicle may be exposed to an enemy radar system (or any other enemysensor) and, in response, may identify a signature exposure modelindicative of how the vehicle may appear to the enemy radar system andselect a maneuver for the vehicle to perform (e.g., to orient vehicle ina particular way) to reduce visibility of the vehicle to the enemy radarsystem.

In some embodiments, a survivability planning system may select a courseof action in response to a threat based on a stage of a vehicle'smission. A mission may have any of numerous stages including, but notlimited to, a detection stage in which the vehicle may be detected by athreat, a tracking stage in which the vehicle may be tracked by thethreat, a firing stage where a vehicle may be fired at, and an impactstage where the vehicle may be hit. A survivability planning system mayselect different courses of action in each of these stages and maychoose to weigh different factors in making the selection. For example,in the detection stage a course of action may be selected to reduceexposure of the vehicle to an enemy sensor and in the firing stage acourse of action may be selected to reduce probability of the vehiclebeing hit.

It should be appreciated, that the survivability planning system mayselect a course of action in response to any suitable type of threatand, more generally, any reference to a threat, herein, may referenceany suitable type of threat. For example, the threat may be a militarythreat such as one or more military vehicles, weapons, sensors, and/orenemy ground troops. As another example, the threat may be a threat in acommercial setting such as another vehicle (e.g., a car, a ship, amotorcycle, etc.) or one or more people (e.g., pedestrians) near or on acollision course with the vehicle. Numerous other examples will beapparent to those skilled in the art. As such, it is contemplated thatembodiments of the present invention may operate in any of numerousenvironments, including military and non-military environments, asaspects of the present invention are not limited in this respect.

In another aspect, the inventors have recognized and appreciated that avehicle may encounter multiple threats at the same time during amission. These threats may have different capabilities and may belocated at different locations, so that an optimal strategy with respectto one threat may lead to undesirable outcomes with respect to anotherthreat. Accordingly, in some embodiments, a survivability planningsystem may model and analyze a scenario in such a manner as tosimultaneously take into account information regarding multiple threats,thereby improving the vehicle's overall survivability. For example, inone implementation, a survivability planning system may assign multiplenumerical measures to each potential action for a vehicle in a givenscenario, where each of the numerical measures may correspond to adifferent threat present in the scenario and may be indicative of aseverity of the threat. Such individual numerical measures may becombined in any suitable way to obtain a combined numerical measure foruse in selecting an appropriate course of action for the vehicle.

In yet another aspect, the inventors have recognized and appreciatedthat the response time of a survivability planning system may besignificantly improved by performing some computationally intensiveanalyses ahead of time, for example, by running scenario-specificsimulations prior to the start of a mission. Accordingly, in someembodiments, a survivability planning system may use not onlysituational data indicative of actual conditions under which a vehicleis operating, but also simulation data obtained from off-linesimulations of various vehicle-to-threat scenarios. For instance, insome embodiments, a survivability planning system may have access tomultiple sets of simulation data corresponding to differentvehicle-to-threat scenarios. During a mission (e.g., in response todetecting one or more pop-up threats), the survivability planning systemmay use situational data to identify a matching vehicle-to-threatscenario, and may use simulation data corresponding to the identifiedscenario to select one or more appropriate actions. In this manner, thesurvivability planning system may be able to analyze and compareoutcomes of different courses of action without having to performextensive computations in real time.

In yet another aspect, the inventors have recognized and appreciatedthat visual representations of mission-related information, such as avehicle's exposure to a particular threat and the vehicle's abilities todetect and attack the threat, may facilitate a human operator'sunderstanding and analysis of a vehicle-to-threat scenario, which may inturn improve the quality of the human operator's decision as to asuitable course of action. Accordingly, in some embodiments, asurvivability planning system may be adapted to visually render vehiclesignature, sensor obscuration, countermeasure effectiveness, vehiclevulnerability, or any other suitable type of information to a humanoperator. For example, visual representations may be rendered based onsituational data regarding a pop-up threat. Alternatively, oradditionally, visual representations may be rendered based on simulationdata to help the human operator understand and compare outcomes ofdifferent courses of action.

Following below are more detailed descriptions of various conceptsrelated to, and embodiments of, inventive systems, methods, andapparatus for improving vehicle survivability. It should be appreciatedthat various concepts introduced above and discussed in greater detailbelow may be implemented in any of numerous ways, as the disclosedconcepts are not limited to any particular manner of implementation. Forinstance, the present disclosure is not limited to the particulararrangements of components shown in the various figures, as otherarrangements may also be suitable. Such examples of specificimplementations and applications are provided solely for illustrativepurposes.

Furthermore, any of the inventive concepts disclosed herein may beimplemented in connection with any of the systems and methods describedin U.S. Pat. No. 7,769,502, titled “Survivability/Attack PlanningSystem,” U.S. Pat. No. 7,848,879, titled “Survivability System,” U.S.Pat. No. 8,005,657, titled “Survivability Mission Modeler,” and U.S.Patent Application Publication No. 2010/0010793 A1, titled “VehicleAspect Control,” all of which are hereby incorporated by reference intheir entireties.

FIG. 1 shows an example of an illustrative survivability planning system100, in accordance with some embodiments. The survivability planningsystem 100 may be used in any suitable vehicle to improve survivabilityin an environment comprising one or more threats to the vehicle.Examples of vehicles include, but are not limited to, airplanes,helicopters, rockets, missiles, gliders, spacecraft, lighter-than-aircraft, hovercraft, cars, trucks, motorcycles, tanks, heavy equipment,naval vessels, watercraft, submarines, and the like. A vehicle may bemanned or unmanned, and may be operated manually or automatically, or bya suitable combination of manual control and automatic control.Furthermore, a vehicle may be owned and/or operated by any suitableentity, such as a military entity, a commercial entity, or a privateentity.

In the example shown in FIG. 1, the survivability planning system 100includes a survivability engine 105 adapted to process situational andsimulation data, and to select one or more appropriate actions for avehicle in a given scenario. The selected actions may be presented asrecommendations to a human operator, who may be onboard the vehicle(e.g., a pilot of an aircraft, a driver of a car, etc.), or controllingthe vehicle remotely. Alternatively, the selected actions may beautomatically implemented to control one or more systems on the vehicle.

The survivability planning system 100 may include one or more componentsthat allow the survivability engine 105 to collect and disseminateinformation. For example, in some embodiments, the survivabilityplanning system 100 may receive input from one or more sensors 110onboard the vehicle and may provide control signals to influence how thesensors collect information. The sensors 110 may sense situationalconditions using any suitable passive and/or active sensingtechnologies, including, but not limited to, radar, IR, sonar, videoimage, laser, and acoustic sensing technologies. For instance, somesensors may be configured to sense operating conditions of the vehicle,such as latitude, longitude, altitude, heading, orientation, speed,acceleration, and changes (and/or rates of changes) in any of suchoperating conditions. Some other sensors may sense environmentalconditions, such as light, humidity, atmospheric pressure, wind speed,and wind direction. Yet some other sensors may provide informationregarding one or more threats that may be present. For example, a targetrecognition sensor may provide information relating to threat type(e.g., a weapons system, another vehicle (e.g., another aircraft,another car, another ship, etc.), and an enemy sensor system), and arange sensor (e.g., radar or laser radar) may estimate a distancebetween the vehicle and a detected threat. Other types of sensors mayalso be suitable, as aspects of the present disclosure are not limitedto the use of any particular type of sensors.

In some further embodiments, the survivability engine 105 may interactwith a human operator onboard the vehicle, or a human operator remotelycontrolling the vehicle, via one or more input/output devices 115.Examples of input/output devices include, but are not limited to,touch-sensitive displays, heads-up displays (HUDs), keyboards, buttons,switches, levers, speakers, headphones, speakers, microphones, videocameras, and tactors configured to tactually present information to ahuman operator by relying on the human operators sense of touch. Some ofthese input/output devices may be coupled with an appropriaterecognition engine, such as a speech, gesture, or handwritingrecognition engine, to recognize input from the human operator.

In yet some further embodiments, the survivability engine 105 mayreceive and transmit information via one or more communication devices120, which may use any suitable communication technologies such as radioand microwave technologies. The communication devices 120 may allow thesurvivability engine 105 to interact with a remote system, such as acommand center or another vehicle, and may allow the survivabilityengine 105 to receive any suitable information (e.g., intelligenceinformation and location information about one or more threats to thevehicle).

In the example shown in FIG. 1, the survivability planning system 100further includes a situational data store 125, a simulation data store130, and a signature data store 145. The situational data store 125 maystore any useful information, such as mission objectives, locations andcapabilities of known or likely threats, vehicle capabilities andvulnerabilities, locations and capabilities of friendly entities,terrain and weather conditions, and weather predictions. Suchinformation may be loaded into the situational data store 125 prior tothe start of a mission, or may be collected and stored during themission. For example, the survivability engine 105 may use inputsreceived from the sensors 110, the input/output devices 115, and thecommunication devices 120 to update the information stored in thesituational data store 125.

The simulation data store 130 may store data obtained from off-linesimulations of various vehicle-to-threat scenarios. For instance, insome embodiments, a simulation may be conducted by building a model of aspecific vehicle-to-threat scenario based on hypothetical situationalconditions and using the model to analyze potential courses of action inthe scenario. The model may be built using any suitable technique orcombination techniques, including, but not limited to, those describedin greater detail below in connection with FIGS. 3 and 4A-B. Likewise,any suitable optimization techniques may be used to identify acceptablecourses of action, such as techniques that seek to decrease cost and/orincrease utility over some suitable horizon (which may be finite orinfinite).

The signature data store 145 may store one or more signature exposuremodels associated with a vehicle. Each signature exposure models mayalso correspond to one or more types of threats. For example, asignature exposure model may correspond to a particular type of enemysensor (e.g., an RF, electro-optical, or IR sensor operating at aparticular band of frequencies). The one or more signature exposuremodels may be created using data generated from offline simulationand/or data collected while using the vehicle (e.g., by measuring energyemitted from and/or reflected by the vehicle using one or more sensors).

In some embodiments, a signature exposure model may represent the way avehicle may appear to a sensor. Each sensor may have a correspondingsignature exposure model because the vehicle may appear differently whenexamined, either passively or actively, at different sets offrequencies. For example, the vehicle may appear differently whenexamined by an RF sensor (e.g., a radar) than when examined by an IRsensor (e.g., an infrared camera).

A signature exposure model may store one or more values associated witha set of relative geometries between the vehicle and a sensor. Forexample, a vehicle may appear differently to a sensor from a nose-onorientation than from a sideways orientation. As such, the signatureexposure model may store one or more values for each pair ofazimuth-elevation coordinates. Some examples of signature exposuremodels are described in greater detail below in connection with FIGS. 6Aand 6B. However, it should be appreciated that information captured by asignature exposure model may be organized and/or indexed in any othersuitable way.

In some embodiments, a simulation may involve the same types of analysescarried out by the survivability engine 105 in real time to analyzeactual situational data, the only difference being some or all of theinputs used in the simulation are hypothetical data. In otherembodiments, a simulation may involve different analyses (e.g., analysesthat the survivability engine 105 may not be able to perform in realtime due to constraints in computational resources). For instance, inone implementation, one or more situational conditions may be modeled asa probability distribution to represent imperfect information oruncertainty. Such an input probability distribution may be sampled toestimate probabilities of certain outcomes, such as a vehicle survivingor not surviving an attack by a threat. This computation may be done inan offline simulation for different input probability distributions, andthe outcome probability distributions may be stored in the simulationdata store 130 in association with the corresponding input probabilitydistributions. In this manner, the survivability engine 105 may be ableto retrieve an appropriate outcome probability distribution byidentifying an input probability distribution that matches actualsituational data, without having to compute the outcome probabilitydistribution in real time.

In the example shown in FIG. 1, the survivability engine 105 furtherinteracts with one or more guidance, navigation, and control (GNC)systems 135 and one or more weapon systems 140. For instance, thesurvivability engine 105 may provide control outputs to the GNC systems135 and the weapon systems 140 to implement one or more actions selectedby the survivability engine 105.

In some embodiments, where the survivability planning system 100 is usedin connection with a UAV, the survivability engine 105 may providecontrol signals to the GNC systems 135 to control one or more parametersof the operations of the UAV, including, but not limited to, flightpath, aspect angle, altitude, and speed. In alternative embodiments,where the survivability planning system 100 is used in connection with amanned vehicle (e.g., an aircraft, a car, a motorcycle, a ship, etc.),outputs of the survivability engine 105 may be presented to a humanoperator as a recommendation or suggestion. Such a recommendation orsuggestion may be presented visually (e.g., on a HUD, a dashboard,etc.), audibly (e.g., via speech synthesis), or in any other suitablemanner. Similarly, the survivability engine 105 may provide controlsignals to the weapon systems 142 to configure the weapon systems 142 todetect, track, and engage one or more threats.

In various embodiments, the survivability engine 105 may present one ormore recommendations for a course of action to a human operator in anysuitable manner. For example, the recommendation may be a recommendationto perform a specific vehicle maneuver (e.g., to realize a particularroute for the vehicle to follow), or any other action or actions relatedto guidance, navigation, and control functions. As another example, therecommendation may be a recommendation to perform any action or actionsrelated to weapon systems of the vehicle. Recommendations may bepresented using any input/output devices of the survivability planningsystem. As such, recommendations may be provided using any suitablemodality including, but not limited to, devices for presentinginformation aurally, visually, and/or tactually.

While examples of various components of a survivability planning systemare discussed above in connection with FIG. 1, it should be appreciatedthat such examples are provided solely for illustrative purposes. Othercombinations of components may also be suitable, as aspects of thepresent disclosure are not so limited.

FIG. 2 shows an illustrative process 200 that may be performed by asurvivability planning system to improve vehicle survivability during amission, in accordance with some embodiments. For example, the process200 may be performed by the survivability engine 105 shown in FIG. 1 toselect one or more appropriate actions in a given scenario.

At act 205, the survivability planning system may obtain situationaldata relating to an actual or hypothetical scenario in which the vehiclemay be operating. The situational data may be received from any suitablesource, such as a sensor, a user input device, or a communicationsdevice, or may be retrieved from a suitable data storage. As discussedabove in connection with FIG. 1, situational data may include anyinformation that may be useful in selecting an appropriate action in thescenario. For example, the situational data may include informationrelating to the vehicle's own capabilities, such as the ability tomaneuver in a certain way under certain conditions, to detect a threat,or to attack a threat. As another example, the situational data mayinclude information relating to known or potential threats that thevehicle may encounter, such as locations and types of such threats. Asyet another example, the situational data may include informationrelating to environmental conditions, such as weather and terrainconditions and locations and capabilities of friendly entities. Othertypes of situational data may also be suitable, as aspects of thepresent disclosure are not limited to the use of any particular types ofsituational data.

At act 210, the survivability planning system may use the situationaldata obtained at act 205 to update an evaluation model for use inselecting one or more appropriate actions for the vehicle in the givenscenario. In some illustrative embodiments, the evaluation model may bea mathematical model that represents the actual or hypothetical scenarioin which the vehicle is operating. Such a model may be constructed usingtechniques described below in connection with FIGS. 3 and 4A-B, or anyother suitable techniques, such as those generally known in the art.

Updating the evaluation model may, in some instances, include building anew model using the situational data obtained at act 205. Alternatively,the evaluation model may have been built previously, in which caseupdating the evaluation model may include modifying, deleting, or addingmodel parameters based on the situational data obtained at act 205. Thesituational data may be used to perform such an update with or withoutfurther processing.

Having updated the evaluation model, the survivability planning systemmay proceed to act 215 to evaluate possible courses of action for thevehicle. This evaluation may be performed using any suitable criterionor combination of criteria. In one example, the survivability planningsystem may seek to identify potential courses of action that maycompletely avoid detection by any threat. In another example, thesurvivability planning system may use a more relaxed criterion, forexample by tolerating some detection so long as no threat would havesufficient time to track and engage the vehicle after detecting thevehicle. In yet another example, the survivability planning system mayuse a combination of different criteria, such as avoiding detection byany threat while maintaining effectiveness of countermeasures againstany threat.

In some embodiments, some of the criteria used by the survivabilityplanning system may be probabilistic. For instance, the survivabilityplanning system may seek to identify potential courses of action forwhich the probability that the vehicle survives the mission is above anaccepted threshold. Such an outcome probability may be derived based onvarious input probabilities, examples of which include, but are notlimited to, a probability that the vehicle can be detected by a threatof a certain type located at a certain angle and a certain distance fromthe vehicle given certain weather and terrain conditions, a probabilitythat the threat can successfully track and engage the vehicle given acertain window of exposure of the vehicle to the threat, and aprobability that the vehicle can survive an attack by the threat.

In some further embodiments, the survivability planning system may usesimulation data to inform the evaluation performed at act 215. Forexample, the survivability planning system may use the situational dataobtained at act 205 to identify one or more matching scenarios for whichsimulation data is available from a suitable data store (e.g., thesimulation data store 130 shown in FIG. 1). The available simulationdata may include useful information such as probabilities of certainoutcomes of interest. In this manner, the survivability planning systemmay be able to compare outcomes of different courses of action withouthaving to perform extensive computations in real time.

Proceeding to act 220, the survivability planning system may select anoptimal course of action for the vehicle in the given scenario based onthe analysis performed at act 215. As discussed above, whether a courseof action is preferred over another course of action may depend on theparticular criterion or criteria employed by the survivability planningsystem. Furthermore, it should be appreciated that an “optimal” courseof action need not be a most preferred course of action in an absolutesense. Rather, the survivability planning system may use any suitableapproximation algorithms or heuristics to select a course of action thatmay be preferred over other courses of action. Such approximationalgorithms or heuristics may be employed to improve response time orreduce computational requirements of the survivability planning system.

Proceeding to act 225, the survivability planning system may determinewhether the vehicle has successfully completed the mission. If so, theprocess 200 may end. Otherwise, the process 200 may return to act 205 toobtain new situational data. In this manner, the survivability planningsystem may continually analyze up-to-date situational data and respondto changes by re-evaluating the vehicle's available options and possiblyselecting a different course of action.

As mentioned above, the process 200 may be used to analyze ahypothetical scenario, which may be done in the context of a simulation.In that case, hypothetical situational data may be obtained at act 205,and one or more outcomes of the analysis performed at act 215 may bestored in association with the corresponding hypothetical situationaldata in a suitable data store (e.g., the simulation data store 130 shownin FIG. 1). Alternatively, the process 200 may be performed by asurvivability planning system during an actual mission, in which caseactual situational data may be obtained at act 205, and the course ofaction selected at act 220 may be implemented or presented to a humanoperator as a recommendation.

Turning to FIG. 3, an example of a mathematical model 300 is illustratedthat can be used to represent a vehicle-to-threat scenario, inaccordance with some embodiments. In this example, a physical region tobe traversed by a vehicle is modeled as a collection of cells arrangedin a two-dimensional grid, and movement of the vehicle through thephysical region is modeled as a sequence of maneuvers between cells.

Each cell in the illustrative model 300 may include a number ofdifferent nodes. For example, as shown in FIG. 3, cell <0,0> (e.g., thecell located at the upper left corner, at row 0 and column 0) mayinclude eight nodes, labeled “0,” “1,” . . . , “7.” Each such node mayrepresent a different angle at which the vehicle may enter cell <0,0>.For example, node 0 may represent the vehicle entering cell <0,0> fromcell <1,0>, and node 7 may represent the vehicle entering cell <0,0>from cell <1,1>.

Furthermore, in the illustrated model 300, the vehicle may exit eachcell in a number of different ways depending on how the vehicle enteredthe cell. For example, as illustrated in FIG. 3, the vehicle may betraveling from left to right at node 34 because the vehicle entered cell<1,1> from cell <1,0>. Assuming the vehicle cannot travel backwards orsuspend its motion (e.g., as typically is the case for a fixed-wingaircraft), the vehicle can perform one of seven possible maneuvers fromnode 34, causing the vehicle to exit cell <1,1> and enter, respectively,cells <0,0>, <0,1>, <0,2>, <1,2>, <2,0>, <2,1>, and <2,2>.

Thus, the illustrative model 300 can be viewed as a state transitionmodel, where each state is defined by some information relating to thevehicle (e.g., current location of the vehicle specified using quantizedcoordinates that divide a continuous physical region into a collectionof discrete cells) and possibly some history information (e.g., how thevehicle entered a cell), and where transitions from given states aredefined by actions the vehicle can perform (e.g., maneuvers that thevehicle is physically capable of performing) from that state.

It should be appreciated that the model 300 shown in FIG. 3 anddescribed above is provided solely for purposes of illustration, asother types of models may also be used in connection with variousinventive concepts disclosed herein. For instance, although theillustrative model 300 is a two-dimensional model, other dimensionalitymay also be used, as aspects of the present disclosure are not limitedto any particular dimensionality. Furthermore, aspects of the presentdisclosure are not limited to any particular arrangement or geometry ofcells, nor to any particular definitions of nodes and transitions.

FIGS. 4A-B show an illustrative route 400 for a vehicle modeled as asequence of maneuvers each associated with a cost metric, in accordancewith some embodiments. In FIG. 4A, the illustrative route 400 is shownas a sequence of maneuvers between cells, whereas FIG. 4B shows anactual path that the vehicle may follow by executing the sequence ofmaneuvers shown in FIG. 4A. The underlying model used in this examplemay be similar to the model 300 described above in connection with FIG.3.

In the example shown in FIG. 4A, the vehicle starts at node 3 whichindicates the vehicle entered cell <0,0> from the upper left corner,thus traveling towards the lower right. The vehicle then transitions, at405-1, from node 3 to node 10, exiting cell <0,0> and entering cell<0,1>. As shown in FIG. 4B, transition 405-1 corresponds to the vehicleexecuting a slight left turn while in cell <0,0>. From node 10, thevehicle transitions, at 405-2, to node 36, exiting cell <0,1> andentering cell <1,1>. As shown in FIG. 4B, transition 405-2 correspondsthe vehicle executing a right turn while in cell <0,1>. From node 36,the vehicle transitions, at 405-3, to node 42, exiting cell <1,1> andentering cell <1,2>. As shown in FIG. 4B, transition 405-3 correspondsthe vehicle executing a left turn while in cell <1,1>. From node 42, thevehicle transitions, at 405-4, to node 61, exiting cell <1,2> andentering cell <2,1>. As shown in FIG. 4B, transition 405-4 correspondsthe vehicle executing a hard right turn while in cell <1,2>. Finally,from node 61, the vehicle transitions, at 405-5, to node 54, exitingcell <2,1> and entering cell <2,0>. As shown in FIG. 4B, transition405-5 corresponds the vehicle executing a slight right turn while incell <2,1>.

As discussed above, a survivability planning system may evaluate asequence of maneuvers by assigning to each maneuver a numerical measure(or metric) indicative of how much positive or negative impact themaneuver may have on the overall survivability of the vehicle. Such anumerical measure may reflect any suitable combination of survivabilityfactors that may affect the vehicle's abilities to detect a threat,evade detection by the threat, remove the threat using a countermeasure,and survive an attack by the threat.

For instance, in some embodiments, each maneuver may be assigned a costmetric that takes into account multiple factors, such as whether themaneuver leads the vehicle closer to or farther away from a threat, andwhether the maneuver changes the orientation of the vehicle in such away that the vehicle is more or less likely to be detected by a threat.As discussed in greater detail below in connection with FIGS. 5 and6A-B, a cost metric may also take into account information regardingmultiple threats, for example, by combining component cost metricsassociated with different threats, where a different weight may beassigned to each component cost metric.

In the example shown in FIG. 4A, the transitions 405-1, . . . , 405-5may each be associated with a cost value (respectfully, +0.5, +1, +2,+1, and −3) representing the potential negative impact associated withthe corresponding maneuver. As discussed above, such costs may beassigned in any suitable manner to take into account various aspects ofvehicle survivability. For example, the illustrative cost pattern shownin FIG. 4A may result from a threat being present in cell <1,2>, so thatcost increases as the vehicle moves closer to cell <1,2>. Furthermore, apositive cost may be incurred at transition 405-4, even though thecorresponding maneuver may take the vehicle away from the threat,because making a hard right turn in cell <1,2> may increase thevehicle's exposure to a sensor of the threat (e.g., a jet plane may bemore susceptible to detection by an IR sensor from behind because of theheat released by the jet engines).

While an illustrative cost pattern is shown in FIG. 4A and describedabove, it should be appreciated that aspects of the present disclosureare not limited to any particular methods for assigning numericalmetrics to transitions in a model. For example, in addition to, orinstead of, measures of cost, measures of utility may also be assignedto transitions to represent potential positive impact. As anotherexample, in some further embodiments, cost metrics may be assigned tocells in a two-dimensional cellular cost map, where each cell maycorrespond to a particular combination of latitudinal and longitudinalcoordinates. The cost metrics may be computed based on any availableinformation that may be relevant for survivability of a vehicle.Examples of such information include, but are not limited to,inter-visibility, RF signature, IR signature, visual signature, andaspect angle to threat. Based on the available information, an amount ofaspect angle variation that can be tolerated by the vehicle may bedetermined for each cell, and a number of aspect slices may be selectedfor consideration. For each selected aspect slice, a minimum exposuremay be computed. For instance, the minimum exposure may be computedbased on the physical maneuvering ability of the vehicle or constraintsimposed by the need to perform other important tasks related to themission. For example, the vehicle may be required to point a sensor in aspecific direction and based on the limitations of where the sensor maypoint, the vehicle may be constrained to a certain range of aspectvariation. An overall cost for the cell may then be computed based on amaximum of the minimums computed for the selected aspect slices. Forexample, the overall cost for the cell may be proportional to themaximum of the minimums computed for the selected aspect slices.

Again referring to FIGS. 4A-B, a total cost for the route 400 may be anysuitable combination of the individual cost values, such as a weightedor un-weighted sum of the individual cost values. For instance, in someimplementations, the individual cost values may be weighted according toimmediacy of the corresponding maneuvers (e.g., the transition 405-1 maybe accorded more weight than the transition 405-3 because the latter isfurther into the future). In this manner, the survivability planningsystem may select an appropriate course of action by searching for asequence of transitions of a given length that may be less costly thanother sequences of the same length.

Although specific examples of modeling techniques are described above inconnection with FIGS. 3 and 4A-B, it should be appreciated that othermodeling techniques may also be suitable, as aspects of the presentdisclosure are not limited to any particular way of modelingvehicle-to-threat scenarios. For example, in an embodiment in whichmeasures of utility are used in addition to, or instead of, measures ofcost, a survivability planning system may search for transitionsequences with high utility and/or low cost.

FIG. 5 shows an illustrative process 500 that may be performed by asurvivability planning system to select a possible course of action fora vehicle in an actual or hypothetical vehicle-to-threat scenario, inaccordance with some embodiments. For example, the process 500 may beperformed by the survivability engine 105 shown in FIG. 1 to select anappropriate course of action in a given scenario.

At act 505, the survivability planning system may obtain signatureexposure information indicative of the vehicle's susceptibility todetection by one or more threats. Because different threats (or even thesame threat) may be equipped with different sensing technologies,different signature exposure models may be included in the signatureexposure information. Furthermore, the signature exposure informationmay depend on a type of the vehicle, because different types of vehiclesmay have different attributes such as size, shape, and engine location.In some embodiments, the survivability planning system may obtainsignature exposure information by retrieving one or more signatureexposure models from the illustrative signature data store 145 shown inFIG. 1 and described above. FIG. 6A shows an illustrative example of twosignature exposure models that can be used to estimate how easily avehicle (e.g., a helicopter 605) can be detected from various angles bya threat, in accordance with some embodiments. In this example, a firstsignature exposure model is associated with IR technology and isrepresented by a curve 610 around the helicopter 605, and a secondsignature exposure model is associated with radar technology and isrepresented by a curve 615 around the helicopter 605.

In the example shown in FIG. 6A, a line is drawn between the helicopter605 and an IR threat 620, intersecting the curve 610 at a point 625. Thedistance between the point 625 and the helicopter 605 may be indicativeof how easily the IR threat 620 can detect the helicopter 605 from theparticular direction shown in FIG. 6A. Thus, in this example, the curve610 indicates that the helicopter 605 is more susceptible to detectionby an IR sensor from the back than from the front, which may be due toheat being released by engines the helicopter 605.

Similarly, in the example shown in FIG. 6A, a line is drawn between thehelicopter 605 and the radar threat 630, intersecting the curve 615 at apoint 635. The distance between the point 635 and the helicopter 605 isindicative of how easily the radar threat 630 can detect the helicopter605 from the particular direction shown in FIG. 6A. Thus, in thisexample, the curve 615 indicates that the helicopter 605 is moresusceptible to detection by a radar from the front and at certain anglesfrom the side, which may be due to the shapes of the nose and the rotorblades.

Returning to FIG. 5, the signature information obtained at act 505 maybe processed, for example, to combine signature exposure models ofdifferent threats. As discussed in greater detail below in connectionwith acts 515 and 520, a combined signature exposure model may be usedto select a course of action that may reduce the vehicle's overallsignature exposure with respect to multiple threats (although theselected course of action may not be optimal with respect to anyindividual threat). The combination may be done in any suitable manner,for example, to reflect desired trade-offs among the different threats.

In one embodiment, as illustrated in FIG. 6B, two different signatureexposure models, represented respectively by the curves 650 and 655, maybe added together to obtain a third signature exposure model,represented by a curve 660. Signature exposure models may be added inany suitable way. For example, two or more signature exposure models maybe added by adding values stored in association with corresponding pairsof azimuth-elevation coordinates in the signature exposure models beingadded. This sum may, although need not, be weighted, where the weightsmay reflect which threat is considered to be more important in a givensituation. For example, as discussed in greater detail below inconnection with FIG. 7, the weights may change depending when thevehicle is believed to have been exposed to a threat and how much timehas elapsed since the initial exposure.

Although specific examples of signature exposure models and methods forcombining signature exposure models are discussed in connection withFIGS. 6A-B, it should be appreciated that the present disclosure is notlimited to any particular type of signature exposure models, nor to anyparticular method of combination. For example, although two-dimensionalsignature exposure models (e.g., curves) are shown in FIGS. 6A-B,signature exposure models may also be three-dimensional (3D), as shownin FIGS. 7-8 and discussed below.

Returning again to FIG. 5, the survivability planning system may obtainconstraint information at act 510. Constraints may be used to modelvarious mission requirements, examples of which include, but are notlimited to, mission deadline, sensor obscuration, vehicle vulnerability,and countermeasure effectiveness. For example, a sensor clearanceobscuration may be imposed to avoid combinations of vehicle location andvehicle orientation that may cause an object (e.g., landing gear, in anembodiment in which the vehicle is an aircraft) to obscure a threat froma sensor on the vehicle. As another example, a vehicle vulnerabilityconstraint may be imposed to avoid combinations of vehicle location andvehicle orientation that may cause a particularly vulnerable portion ofthe vehicle to be exposed to an attack by a threat. As yet anotherexample, a countermeasure effectiveness constraint may be imposed toavoid combinations of vehicle location and vehicle orientation that mayprevent countermeasures from effectively attacking a threat. As yetanother example, a mission deadline constraint may be imposed to requirecompletion of a mission by an absolute deadline or a relative deadline(e.g., relative to a start time of the mission). Evaluating such aconstraint may involve examining mission history, which may berepresented as a sequence of transitions in a state transition model.

As yet another example, a mission stage deadline constraint may beimposed to require completion of a course of action, such as any of thepreviously-described courses of action, before the completion of aparticular stage of the mission. Accordingly, the survivability planningsystem may estimate the duration of one or more mission stages and mayestimate how much time remains in a particular stage of a mission.Mission stages and examples of mission stages are described in moredetail below with reference to FIG. 7.

At act 515, the survivability planning system may compute numerical costmeasures for various actions that may be executed by the vehicle. Aspreviously mentioned, the action may be executed by the vehicle for anysuitable purpose and, for example, may be executed in response todetecting a threat, to avoid the threat, to mitigate/reduce the threat,and/or to eliminate the threat. Some examples of actions that may betaken include, but are not limited to, firing one or more weapons,changing speed, altitude, heading or orientation, performing a maneuver,and deploying a countermeasure (e.g., jamming a communication channel).In some embodiments, such an action may be modeled as a transition fromone state to another in a state transition model, where each state maycontain information indicative of situational conditions at acorresponding point during a mission and possibly information indicativeof mission history up to that point.

The survivability planning system may select an action to be analyzed inany suitable manner. For example, if the process 500 is performed in thecontext of a simulation, the goal may be to analyze all possibleactions, so the survivability planning system may simply select theactions one by one in any suitable order. As another example, theselection may be randomized (e.g., by randomly selecting one or moreaction parameters). As yet another example, the survivability planningsystem may use any suitable strategies or heuristics designed tofacilitate efficient selection of appropriate actions. For instance, inan embodiment in which a state transition model is used, thesurvivability planning system may identify and reduce states and/ortransitions that may lead to indistinguishable outcomes.

In various embodiments, the survivability planning system may computethe numerical cost measures based on any suitable combination offactors. For instance, the survivability planning system may take intoaccount one or more measures of signature exposure (e.g., as discussedabove in connection with act 505) and one or more constraints (e.g., asdiscussed above in connection with act 510). As a more specific example,a higher cost may be associated with an action if the action is likelyto increase the vehicle's signature exposure and/or cause one or moreconstraints to be violated.

In some embodiments, numerical measures corresponding to signatureexposure to different threat sensors and/or numerical measurescorresponding to different constraints may be weighted differentlydepending on time-to-engagement estimates. For example, an individualcost measure corresponding to a threat that is estimated to have hadsufficient time to detect and track the vehicle may be weighted moreheavily compared to an individual cost measure corresponding to a threatthat is estimated not to have had sufficient time to detect and trackthe vehicle. An illustrative example of such a weighting scheme isdescribed in greater detail below in connection with FIG. 7. However, itshould be appreciated that other weighting schemes may also be used, asaspects of the present disclosure are not limited to any particularmethod for combining numerical measures.

Having computed numerical cost measures at act 515, the survivabilityplanning system may proceed to act 520 to select an appropriate courseof action. Any suitable optimization techniques may be used for thatpurpose, such as techniques that seek to decrease cost and/or increaseutility over some suitable horizon (which may be finite or infinite).For example, in an embodiment in which a combined cost is computed foreach action at act 515 to take into account signature exposure withrespect to multiple threat sensors, an optimization technique that seeksto minimize cost may be used to select a course of action that reducesthe risk of the vehicle being detected by any of the threat sensors.Additionally, alternative courses of action (e.g., second-best course ofaction, third-best course of action, etc., as determined by theoptimization technique) may be identified in act 520.

At act 525, the survivability planning system may evaluate the course ofaction selected at act 520. For instance, the survivability planningsystem may check whether any of the constraints discussed above inconnection with act 510 may be violated as a result of executing theselected course of action. For example, the survivability planningsystem may check whether the course of action was completed before thecompletion of a particular mission stage. As another example, thesurvivability planning system may check whether the course of action isa physically-realizable maneuver. Alternatively, or additionally, theselected course of action may be presented to a human operator forreview and approval, where the human operator may be onboard thevehicle, or may be controlling the vehicle remotely.

If it is determined at act 525 that the course of action selected at act520 is satisfactory, the process 500 may end and the selected course ofaction may be executed by the vehicle. If it is determined at act 525that the course of action selected at act 520 is not satisfactory, theprocess 500 may select one of the alternative course of actionidentified at act 520 to determine whether that course of action may besatisfactory. Additionally, or alternatively, the process 500 mayproceed to modify one or more parameters of the evaluation model used atacts 515 and 520. In one example, a different vehicle type may beselected with different maneuver, detection, and/or countermeasurecapabilities. In another example, different mission start time and/orstart location may be selected. Other types of modifications may also besuitable, as aspects of the present disclosure are not limited to anyparticular way of modifying the evaluation model.

Having modified the evaluation model at act 530, the process 500 mayreturn to act 515 to repeat the analysis and select another course ofaction. This may continue until a satisfactory course of action isidentified, or until some stopping condition is reached (e.g., adeadline or a certain number of allowed iterations).

While illustrative techniques for selecting a course of action for avehicle are discussed above in connection with FIG. 5, it should beappreciated that aspects of the present disclosure are not limited toany particular technique or combination of techniques. For example, anysuitable part of the process 500 may be made probabilistic, for example,by replacing deterministic input values with input probabilitydistributions. For example, a probability distribution may be providedover likely locations of each threat, and another probabilitydistribution may be provided over likely types of each threat andassociated detection and attack capabilities. In this manner, possiblecourses of action may be evaluated based on probabilistic criteria(e.g., whether a vehicle is estimated to survive a mission with aprobability higher than some threshold value). As another example, acourse of action may be selected using an approach other than bycomputing numerical cost measures. For instance, a course of action maybe selected applying one or more rules to available situational dataand/or signature exposure models.

As discussed above, the survivability planning system may accorddifferent weights to different factors depending on a stage of amission. FIG. 7 shows an illustrative embodiment in which a missiontimeline is divided into multiple stages, namely, a “safe” stage 705, a“detection” stage 710, a “tracking” stage 715, a “firing” stage 720, an“engagement” stage 725, and an “impact” stage 730. These stages maydiffer in the degree of danger a threat may pose to a vehicle in eachstage.

At the safe stage 705, the survivability planning system may determinethat the vehicle is unlikely to have been detected by any threat, forexample, because each known threat is more than a correspondingthreshold distance (e.g., the distance at which there exists a 10% orlower chance of being detected by the threat) away from the vehicle andsensors onboard the vehicle have not detected any pop-up threat.

At the detection stage 710, the survivability planning system maydetermine that the vehicle is likely to be detected by a threat. Thisstage may be entered when some threat is no more than the correspondingthreshold distance (e.g., the distance at which there exists at least a50% chance of being detected by the threat) away from the vehicle due torelative movement between the vehicle and the threat. Alternatively, thedetection stage 710 may be entered when a sensor onboard the vehicledetects a pop-up threat, or when a notification of the presence of apop-up threat is received via a communication interface. Once thedetection stage 710 is entered, the survivability planning system mayoptimize for reduced visual signature, for example, by assigning agreater weight to a measure of visual signature when computing numericalcost measures at act 515 of FIG. 5. An illustrative representation 712of a measure of visual signature is shown in FIG. 7, having an hourglassshape indicating that the vehicle is more easily seen from above orbelow than from the side.

At the tracking stage 715, the survivability planning system maydetermine that the threat is likely to have detected and begun trackingthe vehicle. This stage may be entered when, for instance, the vehiclehas entered a detection range (e.g., visual range, IR range, radarrange, etc.) of the threat for more than a corresponding thresholdperiod of time (e.g., the time at which there exists at least a 50%chance that the threat has begun tracking the vehicle). Alternatively,the tracking stage 715 may be entered when a sensor onboard the vehicleconfirms that the threat has detected the vehicle. Once the trackingstage 715 is entered, the survivability planning system may optimize forreduced signature (e.g., visual signature, IR signature, radarsignature, etc.), for example, by assigning a greater weight to ameasure of signature (e.g., visual signature, IR signature, radarsignature, etc.) when computing numerical cost measures at act 515 ofFIG. 5. An illustrative representation 717 of a measure of IR signatureis shown in FIG. 7 in association with the tracking stage 715,indicating that the vehicle is more easily tracked from behind than fromthe front by an IR sensor.

It should be appreciated that aspects of the present disclosure are notlimited to the use of signature exposure measures based on anyparticular detection technology. Rather, signature exposure measuresbased on any suitable detection technology, or combination of detectiontechnologies, may be used to evaluate different courses of actions for avehicle. Furthermore, aspects of the present disclosure are not limitedto the use of signature exposure measures, as other types of measuresmay also be suitable. For example, any suitable measure may be used torepresent aspect- and/or elevation-dependent data around a vehicle, suchas data relating one or more constraints (e.g., sensor obscuration),data relating one or more transmission patterns (e.g., the vehicle'sability to jam, or transmit over, a communication channel), datarelating to countermeasure effectiveness, data relating to hitsurvivability, and any suitable combination thereof. Examples of suchmeasures are discussed below in connection with the firing stage 720,the engagement stage 725, and the impact stage 730 of FIG. 7.

At the firing stage 720, the survivability planning system may determinethat the threat is likely to have locked on to the vehicle and bepreparing to fire. This stage may be entered when, for instance, thetracking stage 715 has lasted for more than a corresponding thresholdperiod of time (e.g., the time at which there exists at least a 50%chance of lock occurring). Alternatively, the firing stage 720 may beentered when a sensor onboard the vehicle confirms that the threat haslocked on to the vehicle. Once the firing stage 720 is entered, thesurvivability planning system may optimize for countermeasureeffectiveness, for example, by assigning a greater weight to a measureof countermeasure effectiveness when computing numerical cost measuresat act 515 of FIG. 5. An illustrative representation 722 of a measure ofcountermeasure effectiveness is shown in FIG. 7, indicating that atarget below the vehicle can be attacked more effectively than a targetabove the vehicle, and that a threat located at certain angles from thevehicle (e.g., through an opening 724 in the representation 722) cannotbe effective attacked.

At the engagement stage 725, the survivability planning system maydetermine that the threat is likely to have engaged the vehicle. Thisstage may be entered when, for instance, the firing stage 720 has lastedfor more than a corresponding threshold period of time (e.g., the timeat which there exists at least a 50% chance of the threat firing aweapon at the vehicle). Alternatively, the engagement stage 725 may beentered when a sensor onboard the vehicle confirms that a weapon of thethreat has fired against the vehicle. Once the engagement stage 720 isentered, the survivability planning system may optimize for hitsurvivability, for example, by assigning a greater weight to a measureof hit survivability when computing numerical cost measures at act 515of FIG. 5. An illustrative representation 727 of a measure of hitsurvivability is shown in FIG. 7, having various spikes corresponding toportions of the vehicle that are more vulnerable than others. Forexample, a spike 728 may indicate that the vehicle is particularlyvulnerable to a hit from the front.

At the impact stage 730, the survivability planning system may determinethat the threat has engaged the vehicle and impact is imminent, and maycontinue to optimize for hit survivability, as in the engagement stage725. The impact stage 730 may be entered when, for instance, theengagement stage 725 has lasted for more than a corresponding thresholdperiod of time (e.g., the time at which there exists at least a 50%chance of impact occurring). Alternatively, the impact stage 730 may beentered when a sensor onboard the vehicle confirms that a missile firedby the threat is less than a corresponding threshold distance away fromthe vehicle.

While an illustrative timeline and associated optimization strategiesare discussed above in connection with FIG. 7, it should be appreciatedthat aspects of the present disclosure are not limited to the use of atimeline, nor to any particular optimization strategy. Furthermore, inan embodiment in which a timeline is used, transitions between variousstages may be defined in any suitable manner. For instance, any of thestages described above may be entered based on input from a humanoperator onboard the vehicle, instead of, or in addition to, analysis ofautomatically collected data.

FIG. 8 shows an illustrative graphical user interface (GUI) 800 that maybe used to visually render a quantity of interest to a human operator,in accordance with some embodiments. For example, in some embodiments,the GUI 800 may be used to visually render a numerical measureindicative of a vehicle's signature exposure to a threat sensor or anumerical measure corresponding to a constraint.

In the example shown in FIG. 8, the GUI 800 is used to render a 3D spacein a 2D representation of a vehicle's signature exposure to a radar. The2D representation is in the form of a surface 805 surrounding arepresentation 810 of the vehicle, so that, in any given direction, adistance between the surface 805 and the representation 810 may beindicative of a level of signature exposure in that particulardirection. For example, as indicated by the presence of a protrusion815, the vehicle may be more susceptible to detection by a radar fromthe rear due to the shape of the nose of the vehicle. In this manner, ahuman operator may be able to more quickly and easily interpretnumerical measures computed by a survivability planning system.

FIG. 9 shows another illustrative GUI 900 that may be used to visuallyrender a quantity of interest to a human operator, in accordance withsome embodiments. For example, as with the illustrative GUI 800, the GUI900 may be used to visually render a numerical measure indicative of avehicle's signature exposure to a threat sensor or a numerical measurecorresponding to a constraint.

In the example shown in FIG. 9, the GUI 900 is used to render a 3D spacein a 2D representation of an obscuration constraint associated with asensor on a vehicle. The 2D representation is in the form of a surface905 surrounding a representation 910 of the vehicle, so that, in anygiven direction, a distance between the surface 905 and therepresentation 910 may be indicative of whether the sensor may beobscured by some object from sensing in that direction. For instance, asillustrated by a line 915 between the representation 910 of the vehicleand a representation 920 of a threat, intersecting the surface 905 at apoint 925, the distance between the surface 905 and the representation910 may be non-zero, which may indicate that the sensor on the vehicleis not obscured in that direction. By contrast, as indicated by thepresence of “holes” in the surface 905, such as a “hole” 935, the sensorin this example may be obscured by the vehicle's landing gear fromsensing in certain directions.

It should be appreciated that the GUI 800 shown in FIG. 8 and the GUI900 shown in FIG. 9 are merely illustrative, as other types of GUIs mayalso be suitable. For example, instead of rending a 2D representation ofa 3D space, a 2D representation may be rendered of a 2D space (e.g., asshown in FIG. 6AB). Furthermore, instead of rending a 2D representationas a mesh or shadow, the 2D representation may be rendered in any othersuitable manner, including, but not limited to, as a solid surface.Further still, a representation may be rendered against any suitablebackground, such as an image of the terrain over which the vehicle istraveling, which may be a computer generated image, a satellite image,or any other suitable types of images. However, in alternativeembodiments, background images may not be used at all.

FIG. 10 shows, schematically, an illustrative computer 1000 on whichvarious inventive aspects of the present disclosure may be implemented.The computer 1000 includes a processor or processing unit 1001 and amemory 1002 that may include volatile and/or non-volatile memory. Thecomputer 1000 may also include storage 1005 (e.g., one or more diskdrives) in addition to the system memory 1002. The memory 1002 may storeone or more instructions to program the processing unit 1001 to performany of the functions described herein. The memory 1002 may also storeone more application programs and/or Application Programming Interface(API) functions.

The computer 1000 may have one or more input devices and/or outputdevices, such as devices 1006 and 1007 illustrated in FIG. 10. Thesedevices can be used, among other things, to present a user interface.Examples of output devices that can be used to provide a user interfaceinclude printers or display screens for visual presentation of outputand speakers or other sound generating devices for audible presentationof output. Examples of input devices that can be used for a userinterface include keyboards and pointing devices, such as mice, touchpads, and digitizing tablets. As another example, a computer may receiveinput information through speech recognition or in other audible format.

As shown in FIG. 10, the computer 1000 may also comprise one or morenetwork interfaces (e.g., the network interface 1010) to enablecommunication via various networks (e.g., the network 1020). Examples ofnetworks include a local area network or a wide area network, such as anenterprise network or the Internet. Such networks may be based on anysuitable technology and operate according to any suitable protocol, andmay include wireless networks, wired networks or fiber optic networks.

Having thus described several embodiments of inventive concepts, it isto be appreciated that various alterations, modifications, andimprovements will readily occur to those skilled in the art. Suchalterations, modifications, and improvements are intended to be withinthe spirit and scope of the inventive concepts disclosed herein.Accordingly, the foregoing description and drawings are by way ofexample only.

The above-described embodiments of can be implemented in any of numerousways. For example, the embodiments may be implemented using hardware,software or a combination thereof. When implemented in software, thesoftware code can be executed on any suitable processor or collection ofprocessors, whether provided in a single computer or distributed amongmultiple computers.

Also, the various methods or processes outlined herein may be coded assoftware that is executable on one or more processors that employ anyone of a variety of operating systems or platforms. Additionally, suchsoftware may be written using any of a number of suitable programminglanguages and/or programming or scripting tools, and also may becompiled as executable machine language code or intermediate code thatis executed on a framework or virtual machine.

In this respect, the invention may be embodied as a non-transitorycomputer readable medium (or multiple computer readable media) (e.g., acomputer memory, one or more floppy discs, compact discs, optical discs,magnetic tapes, flash memories, circuit configurations in FieldProgrammable Gate Arrays or other semiconductor devices, or othernon-transitory, tangible computer storage medium) encoded with one ormore programs that, when executed on one or more computers or otherprocessors, perform methods that implement the various embodiments ofthe invention discussed above. The computer readable medium or media canbe transportable, such that the program or programs stored thereon canbe loaded onto one or more different computers or other processors toimplement various aspects of the present invention as discussed above.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that can be employed to program a computer or otherprocessor to implement various aspects of the present invention asdiscussed above. Additionally, it should be appreciated that accordingto one aspect of this embodiment, one or more computer programs thatwhen executed perform methods of the present invention need not resideon a single computer or processor, but may be distributed in a modularfashion amongst a number of different computers or processors toimplement various aspects of the present invention.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. That perform particular tasks or implement particularabstract data types. Typically the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in anysuitable form. For simplicity of illustration, data structures may beshown to have fields that are related through location in the datastructure. Such relationships may likewise be achieved by assigningstorage for the fields with locations in a computer-readable medium thatconveys relationship between the fields. However, any suitable mechanismmay be used to establish a relationship between information in fields ofa data structure, including through the use of pointers, tags or othermechanisms that establish relationship between data elements.

Various aspects of the present invention may be used alone, incombination, or in a variety of arrangements not specifically discussedin the embodiments described in the foregoing and is therefore notlimited in its application to the details and arrangement of componentsset forth in the foregoing description or illustrated in the drawings.For example, aspects described in one embodiment may be combined in anymanner with aspects described in other embodiments.

Also, the invention may be embodied as a method, of which an example hasbeen provided. The acts performed as part of the method may be orderedin any suitable way. Accordingly, embodiments may be constructed inwhich acts are performed in an order different than illustrated, whichmay include performing some acts simultaneously, even though shown assequential acts in illustrative embodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed, but are usedmerely as labels to distinguish one claim element having a certain namefrom another element having a same name (but for use of the ordinalterm) to distinguish the claim elements.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

What is claimed is:
 1. A system for improving vehicle survivability,comprising at least one processor programmed to: build an evaluationmodel based at least in part on mission information, the missioninformation comprising vehicle information regarding at least onevehicle, the mission information further comprising threat informationregarding a plurality of threats to the at least one vehicle, whereinthe evaluation model is based on a plurality of cells that collectivelyrepresent a physical region to be traversed by the at least one vehicle;use the evaluation model to assign a numerical measure to each potentialaction of a plurality of potential actions for the at least one vehicle,the numerical measure being based at least in part on first and secondmeasures of signature exposure of the at least one vehicle resultingfrom executing the potential action, wherein the first measure ofsignature exposure relates to a first threat of the plurality of threatsand the second measure of signature exposure relates to a second threatof the plurality of threats; and select, based at least in part on therespective numerical measures assigned to the plurality of potentialactions, a sequence of actions to be executed by the at least onevehicle to improve the at least one vehicle's survivability against theplurality of threats, wherein the sequence of actions comprises a firstaction by which the at least one vehicle is to enter a first cell in thephysical region and a second action by which the at least one vehicle isto leave the first cell and enter a second cell in the physical region.2. The system of claim 1, wherein: the vehicle information comprises atleast one constraint; for at least one potential action of the pluralityof potential actions, the corresponding numerical measure is furtherbased on whether the at least one constraint is violated as a result ofexecuting the at least one potential action.
 3. The system of claim 2,wherein the at least one constraint comprises an obscuration constraintfor limiting obscuration of at least one sensor of the at least onevehicle with respect to at least one threat of the plurality of threats.4. The system of claim 2, wherein the at least one constraint comprisesa countermeasure effectiveness constraint for maintaining effectivenessof at least one countermeasure of the at least one vehicle against atleast one threat of the plurality of threats.
 5. The system of claim 2,wherein the at least one constraint comprises a vulnerability constraintfor reducing exposure of at least one vulnerable portion of the at leastone vehicle to at least one threat of the plurality of threats.
 6. Thesystem of claim 1, wherein the at least one processor is furtherprogrammed to: visually render a representation of the at least onevehicle in an N-dimensional space; and visually render an(N−1)-dimensional object in the N-dimensional space, wherein, for eachpoint on the (N−1)-dimensional object, a distance between therepresentation of the at least one vehicle and the point on the(N−1)-dimensional object is indicative of a quantity of interest along adirection from the representation of the at least one vehicle to thepoint on the (N−1)-dimensional object.
 7. The system of claim 6, whereinthe quantity of interest is indicative of whether at least one sensor ofthe at least one vehicle is obscured along the direction from therepresentation of the at least one vehicle to the point on the(N−1)-dimensional object.
 8. The system of claim 6, wherein the quantityof interest is indicative of how vulnerable the at least one vehicle isto a hit from the direction from the point on the (N−1)-dimensionalobject to the representation of the at least one vehicle.
 9. A methodfor improving vehicle survivability, the method comprising acts of:building, by at least one processor, an evaluation model based at leastin part on mission information, the mission information comprisingvehicle information regarding at least one vehicle, the missioninformation further comprising threat information regarding a pluralityof threats to the at least one vehicle, wherein the evaluation model isbased on a plurality of cells that collectively represent a physicalregion to be traversed by the at least one vehicle; using, by the atleast one processor, the evaluation model to assign a numerical measureto each potential action of a plurality of potential actions for the atleast one vehicle, the numerical measure being based at least in part onfirst and second measures of signature exposure of the at least onevehicle resulting from executing the potential action, wherein the firstmeasure of signature exposure relates to a first threat of the pluralityof threats and the second measure of signature exposure relates to asecond threat of the plurality of threats; and selecting, by the atleast one processor, based at least in part on the respective numericalmeasures assigned to the plurality of potential actions, a sequence ofactions to be executed by the at least one vehicle to improve the atleast one vehicle's survivability against the plurality of threats,wherein the sequence of actions comprises a first action by which the atleast one vehicle is to enter a first cell in the physical region and asecond action by which the at least one vehicle is to leave the firstcell and enter a second cell in the physical region.
 10. The method ofclaim 9, wherein: the vehicle information comprises at least oneconstraint; for at least one potential action of the plurality ofpotential actions, the corresponding numerical measure is further basedon whether the at least one constraint is violated as a result ofexecuting the at least one potential action.
 11. The method of claim 10,wherein the at least one constraint comprises an obscuration constraintfor limiting obscuration of at least one sensor of the at least onevehicle with respect to at least one threat of the plurality of threats.12. The method of claim 10, wherein the at least one constraintcomprises a countermeasure effectiveness constraint for maintainingeffectiveness of at least one countermeasure of the at least one vehicleagainst at least one threat of the plurality of threats.
 13. The methodof claim 10, wherein the at least one constraint comprises avulnerability constraint for reducing exposure of at least onevulnerable portion of the at least one vehicle to at least one threat ofthe plurality of threats.
 14. The method of claim 9 further comprising:visually rendering a representation of the at least one vehicle in anN-dimensional space; and visually rendering an (N−1)-dimensional objectin the N-dimensional space, wherein, for each point on the(N−1)-dimensional object, a distance between the representation of theat least one vehicle and the point on the (N−1)-dimensional object isindicative of a quantity of interest along a direction from therepresentation of the at least one vehicle to the point on the(N−1)-dimensional object.
 15. The method of claim 14, wherein thequantity of interest is indicative of whether at least one sensor of theat least one vehicle is obscured along the direction from therepresentation of the at least one vehicle to the point on the(N−1)-dimensional object.
 16. The method of claim 14, wherein thequantity of interest is indicative of how vulnerable the at least onevehicle is to a hit from the direction from the point on the(N−1)-dimensional object to the representation of the at least onevehicle.
 17. At least one non-transitory computer-readable storagemedium storing processor-executable instructions that, when executed byat least one processor, cause the at least one processor to: build anevaluation model based at least in part on mission information, themission information comprising vehicle information regarding at leastone vehicle, the mission information further comprising threatinformation regarding a plurality of threats to the at least onevehicle, wherein the evaluation model is based on a plurality of cellsthat collectively represent a physical region to be traversed by the atleast one vehicle; use the evaluation model to assign a numericalmeasure to each potential action of a plurality of potential actions forthe at least one vehicle, the numerical measure being based at least inpart on first and second measures of signature exposure of the at leastone vehicle resulting from executing the potential action, wherein thefirst measure of signature exposure relates to a first threat of theplurality of threats and the second measure of signature exposurerelates to a second threat of the plurality of threats; and select,based at least in part on the respective numerical measures assigned tothe plurality of potential actions, a sequence of actions to be executedby the at least one vehicle to improve the at least one vehicle'ssurvivability against the plurality of threats, wherein the sequence ofactions comprises a first action by which the at least one vehicle is toenter a first cell in the physical region and a second action by whichthe at least one vehicle is to leave the first cell and enter a secondcell in the physical region.
 18. The at least one non-transitorycomputer-readable storage medium of claim 17, wherein: the vehicleinformation comprises at least one constraint; for at least onepotential action of the plurality of potential actions, thecorresponding numerical measure is further based on whether the at leastone constraint is violated as a result of executing the at least onepotential action.
 19. The at least one non-transitory computer-readablestorage medium of claim 18, wherein the at least one constraintcomprises an obscuration constraint for limiting obscuration of at leastone sensor of the at least one vehicle with respect to at least onethreat of the plurality of threats.
 20. The at least one non-transitorycomputer-readable storage medium of claim 18, wherein the at least oneconstraint comprises a countermeasure effectiveness constraint formaintaining effectiveness of at least one countermeasure of the at leastone vehicle against at least one threat of the plurality of threats.